Lemma mapping to universal ontologies in computer natural language processing

ABSTRACT

A method of mapping ontologies between languages may include receiving a first ontology in a first language, where the first ontology includes a first plurality of lemmas and a plurality of relationships between the plurality of lemmas. The method may also include receiving a second plurality of lemmas in a second language, and mapping each of the second plurality of lemmas in the second language to a respective lemma in the first plurality of lemmas in the first language. The method may additionally include generating a second ontology in the second language by using the plurality of relationships in the first ontology to create relationships between the second plurality of lemmas in the second language.

CROSS REFERENCES

This application claims the benefit of U.S. Provisional Application No.62/077,868 filed on Nov. 10, 2014 entitled “Automatic Batch Generationof Concept Relations from N-Grams from Linguistic Input Data.” Thisapplication also claims the benefit of U.S. Provisional Application No.62/077,887 filed on Nov. 10, 2014 entitled “Lemma Mapping to UniverasalOntologies.” Each of these applications is hereby incorporated herein byreference for all purposes.

The following three applications are related to each other and are filedon the same date of Jul. 6, 2015: U.S. Ser. No. 14/______ filed on Jul.7, 2015 entitled “Automatic Generation of N-Grams and Concept RelationsFrom Linguistic Input Data” to Fabrice Nauze et al. (Atty. Dkt. No.88325-934160); U.S. Ser. No. 14/______ filed on Jul. 7, 2015 entitled“Automatic Ontology Generation for Natural-Language ProcessingApplications” to Margaret Salome et al. (Atty. Dkt. No. 88325-913826);and U.S. Ser. No. 14/______ filed on Jul. 7, 2015 entitled “LemmaMapping to Universal Ontologies in Computer Natural-Language Processing”to Fabrice Nauze et al. (Atty. Dkt. No. 88325-934161). Each of theseapplications are hereby incorporated by reference.

BACKGROUND

In a general sense, an ontology is the philosophical study of basicconcepts and their relations to each other. Ontology's deal withquestions concerning what entities can be said to exist, how suchentities can be logically grouped together in a hierarchy, and whatsimilarities and/or differences can be used to segregate ontologicalconcept groups from each other. In computer and information science, thegeneral ontology translates into a naming and definition of types,properties, and relationships that fundamentally exist in a particularcomputing domain. For example, an ontology can compartmentalizevariables needed for a set of computations and establish relationshipsbetween those variables.

BRIEF SUMMARY

In one embodiment, a method of mapping ontologies between languages mayinclude receiving a first ontology in a first language, where the firstontology includes a first plurality of lemmas and a plurality ofrelationships between the plurality of lemmas. The method may alsoinclude receiving a second plurality of lemmas in a second language, andmapping each of the second plurality of lemmas in the second language toa respective lemma in the first plurality of lemmas in the firstlanguage. The method may additionally include generating a secondontology in the second language by using the plurality of relationshipsin the first ontology to create relationships between the secondplurality of lemmas in the second language.

In another embodiment, a non-transitory computer-readable medium may bepresented.

The computer-readable memory may comprise a sequence of instructionswhich, when executed by one or more processors, causes the one or moreprocessors to perform operations including receiving a first ontology ina first language, where the first ontology is comprised of a firstplurality of lemmas and a plurality of relationships between theplurality of lemmas. The operations may also include receiving a secondplurality of lemmas in a second language, and mapping each of the secondplurality of lemmas in the second language to a respective lemma in thefirst plurality of lemmas in the first language. The operations mayadditionally include generating a second ontology in the second languageby using the plurality of relationships in the first ontology to createrelationships between the second plurality of lemmas in the secondlanguage.

In yet another embodiment, a system may be presented. The system mayinclude one or more processors and a memory communicatively coupled withand readable by the one or more processors. The memory may comprise asequence of instructions which, when executed by the one or moreprocessors, cause the one or more processors to perform operationsincluding receiving a first ontology in a first language, where thefirst ontology is comprised of a first plurality of lemmas and aplurality of relationships between the plurality of lemmas. Theoperations may also include receiving a second plurality of lemmas in asecond language, and mapping each of the second plurality of lemmas inthe second language to a respective lemma in the first plurality oflemmas in the first language. The operations may additionally includegenerating a second ontology in the second language by using theplurality of relationships in the first ontology to create relationshipsbetween the second plurality of lemmas in the second language.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the presentinvention may be realized by reference to the remaining portions of thespecification and the drawings, wherein like reference numerals are usedthroughout the several drawings to refer to similar components. In someinstances, a sub-label is associated with a reference numeral to denoteone of multiple similar components. When reference is made to areference numeral without specification to an existing sub-label, it isintended to refer to all such multiple similar components.

FIG. 1 illustrates a diagram of an ontology for use in natural languageprocessing, according to some embodiments.

FIG. 2A illustrates parallel language analysis pipelines for generatingtwo different ontologies, according to some embodiments.

FIG. 2B illustrates a language analysis pipeline using a universalontology, according to some embodiments.

FIG. 2C illustrates a language analysis pipeline using lemmatranslation, according to some embodiments.

FIG. 3A illustrates a diagram of an ontology during lemma mapping,according to some embodiments.

FIG. 3B illustrates a diagram of an ontology during relationshipmapping, according to some embodiments.

FIG. 4A illustrates a system for generating an ontology using asynonymous word database, according to some embodiments.

FIG. 4B illustrates a system for generating an ontology using a languagetranslation service database, according to some embodiments.

FIG. 5 illustrates a flowchart of a method for generating ontologies foruse in natural language processing, according to some embodiments.

FIG. 6 illustrates a simplified block diagram of a distributed systemfor implementing some of the embodiments.

FIG. 7 illustrates a simplified block diagram of components of a systemenvironment by which services provided by the components of anembodiment system may be offered as cloud services.

FIG. 8 illustrates an exemplary computer system, in which variousembodiments may be implemented.

DETAILED DESCRIPTION

Ontologies, hierarchies, and taxonomies are often used in search systemsand information retrieval systems in general to improve recall. Knowingfor instance that “BMW” is a “car brand” can improve recall in searchtasks, increase precision in classification tasks, and so forth.Ontologies are usually built as language-specific modules, first linkingword forms to lemmas, and then linking lemmas to other lemmas withontological/hierarchical relationships. However, as described herein,the relationships linking lemmas together in an ontology can beabstracted into a largely language-independent module. In theembodiments described herein, a method to achieve this abstraction ispresented such that an ontology can be formed in a second language basedon an existing ontology in a first language.

In some embodiments, a process may begin by selecting or defining afirst ontology. This first ontology, referred to as a “ground truth,” or“univeral” ontology can be formed in a lingua franca, such as English.This first ontology may describe relationships between concepts coded aslemmas. The process may then utilize a linguistic analysis pipeline thatis triggered after the segmentation/tokenization of an input corpus, apart-of-speech analysis, and a lemmatization of its tokens. This processcan then define a mapping between lemmas (and optionally theirpart-of-speech) in the first ontology to a lemma in the second ontology.By using the universal ontology to map lemmas and relationships for eachlanguage, the time needed to implement a new language ontology isminimized, and the overall quality across languages can be improvedbecause the quality of the ontology is not tied to the work of theperson coding relationships in a particular language.

In some embodiments, the process can build a language-independentontology as the first ontology. The first ontology includes relationsbetween concepts coded as lemmas. For instance, a “tandem” is a“bicycle”, and a “bicycle” is a “vehicle”. Next, the process can receivea selection of a second language that is different from the firstlanguage of the first ontology. The second language may be a desiredlanguage for the second ontology. Next, the process can generate amapping between words in the first language and the second language. Theprocess can also utilize a database that maps common meanings acrosslanguages. This mapping can either be generic, such that a word in thefirst language is mapped to a corresponding word in the second language,or can be more specific such that mappings between parts-of-speech (PoS)are facilitated. For example, a generic mapping may result in“lemma(second language)→lemma(first language),” while apart-of-speech-specific mapping may result in “PoS+lemma(secondlanguage)→lemma(first language).”

After mapping lemmas between the first language in the second language,the mapped lemmas can be passed on to the next step in the languageanalysis pipeline. Specifically, the relationships between lemmas in thefirst ontology can then be mapped directly using the relationshipsbetween lemmas in the second ontology. In essence, the relationshipsdefined in the first ontology are used to link together lemmas in thesecond ontology to form corresponding relationships. The processautomatically creates word-to-word mappings between the two ontologies,and then uses those mappings to establish relationships in the secondontology. In this manner, the first ontology is used as a template forthe second ontology, with words from the second language filling in thenodes between relationships in the template. Thus, users do not need tomanually assign relationships or determine a vocabulary for the secondontology.

This process can also be constantly updated over time. The firstontology may be associated with a particular corpus, such as a websiteor web domain. As the corpus changes over time (e.g. the webpages areupdated), the first ontology may also change in the first language. Newvocabulary may be added, old vocabulary may be removed, andrelationships may be adjusted. When this happens, new vocabulary in thesecond language can be added to the second ontology or deprecatedvocabulary can be removed from the second ontology. Relationships canalso be adjusted in the second ontology according to adjustedrelationships in the first ontology. This automated process can generatea plurality of language-specific ontologies based on the first ontologythat are automatically kept up-to-date.

FIG. 1 illustrates a diagram 100 of an ontology for use in naturallanguage processing, according to some embodiments. Diagram 100 canrepresent the universal ontology that groups concepts together bymeaning and relates those concepts to other concepts throughrelationships. For example, a bike 114 may represent the concept of atwo wheeled, manually powered vehicle. The ontology may also include alist of other words, or synonyms 104, that describe the same concept ofa bike 114. The bike 114 may be related to other concepts in ahierarchical fashion. For example, the bike 114 is a vehicle 102, whichalso has set of synonyms that can be used to express the concept of avehicle 102. The parent-child relationship in the hierarchy representsan “is a type of relationship between the parent and the child. Asanother example, a dirt bike 106, a tandem bike 108, and a touring bike110 are all types of the parent node representing a bike 114.

The universal ontology is built using concepts represented by one ormore words. Is important to note, however, that individualwords—particularly in the English language—may represent multipleconcepts. In one example, the term “bike” may represent both a noun anda verb. The ontology illustrated by FIG. 1 also includes a secondconcept where the term “bike” is used as a verb, as in “to bike.” Aswith its noun counterpart, the verb bike 116 concept also includes a setof synonyms 112 that may also be used to represent the same concept. Theverb bike 116 is a child of—and therefore has an “is a type ofrelationship with—the parent verb “move” 118.

As will be discussed below, when using a universal ontology to mapconcepts between languages, is often useful to distinguish a concept notonly by the word used in that language, but also by the part of speech.As used herein, these concepts are referred to as “lemmas.” Lemmas inthe universal ontology can be mapped to lemmas in a language-specificontology by using a mapping engine that connects concepts betweenvarious languages. Example mapping engines are discussed below in FIG.4A-4B. Also, the universal ontology illustrated in FIG. 1 can representconcepts and relationships in a language-independent fashion. While theuniversal ontology is displayed in English for illustrative purposesonly, the language could be used for the universal ontology.

FIG. 2A illustrates parallel language analysis pipelines 200 a forgenerating two different ontologies, according to some embodiments. Alanguage analysis pipeline can be used to generate a language-specificontology from a corpus 202. A corpus may include a web domain, a set ofliterature, a technical document, and so forth. In general, a corpus issimply a subset of a particular language that is used in a particularcontext. For example, a corpus may include a web domain for an airline,where terms common to the airline industry are used extensively. Byusing a particular corpus that is limited to a subset of a generallanguage, an ontology may be derived that is specific to the corpus 202,that is more efficient and smaller than a general language ontology, andthat only includes definitions that are specific to the corpus 202, thusspeeding up recall and look up efficiency.

Traditionally, two passes through a language-analysis pipeline would beneeded to generate to ontologies in different languages. In thisexample, an English-language-analysis pipeline and aFrench-language-analysis pipeline would be needed to generate an Englishontology 214-1 and a French ontology 214-2, respectively. An Englishcorpus 202-1 and a French corpus 202-2 would need to be provided to thepipelines for analysis. In some cases, the English corpus 202-1 and theFrench corpus 202-2 could represent the same substantive content indifferent translations. For example, each corpus 202 could represent thesame webpage translated into different languages. To generate theseparate ontologies, each corpus needs to be run through alanguage-analysis pipeline separately.

The process for generating an ontology 214 from a corpus 202 is mayproceed as follows. The corpus 202 can be provided to a corpus analysisengine 204 that isolates text of interest within the corpus 202. Thecorpus analysis engine 204 can remove metadata, comments, display code,and/or other non-substantive text to generate a list of single wordsthat may be considered for the ontology 214. For example, for a webdomain, the corpus analysis engine 204 can scrub the HTML formattingcode, the developer comments, the metadata, the attributes, and/or thelike, and the only text that is displayed on screen to a user for madeavailable to a search engine.

Next, a lemma generation engine 206 can receive the single words andgenerate lemmas. Lemmas can be made up of single words or combinationsof single words forming n-grams. For example, for a website describingan email service, the term “email” would be a single-word lemma, whilethe term “search filter” would be a two-word lemma referring to a singleconcept. The lemma generation engine 206 can receive input that definesthe maximum and/or minimum number n for generating n-grams. For example,the input could define a minimum number 1 and a maximum number 4 forcreating n-grams. The lemma generation engine 206 would then scan thetext made available by the corpus analysis engine 204 and generate alist of possible lemmas that occur as consecutive, single-or multi-wordcombinations in the text.

A lemma filtering/consolidation engine 208 can receive the list ofpossible lemmas from the lemma generation engine 206 and subsequentlypare down the candidate lemmas to generate a final list of lemmas thatwill appear in the ontology 214. The lemma filtering/consolidationengine 208 may include a number of parameter definitions that can beused to filter the list of candidate lemmas. For example, one parametermay define a usage frequency in the corpus required for a candidatelemma to be retained in the list of ontology lemmas. Candidate lemmasthat only occur once or twice in the corpus 202 may be determined to bea grouping of individual words that does not convey a broader meaning orto convey meaning that is not useful in the ontology 214. Thus,candidate lemmas that should be retained may be required to occur atleast a minimum number of times in the corpus 202. Other parameters maydefine dictionaries or other available lemma databases against which thecandidate lemmas can be compared. This allows lemmas that matchpreviously known lemmas to be retained and others to be discarded or putthrough further processing. In some embodiments, the lemmafiltering/consolidation engine 208 can generate a display for a userinterface such that a user can inspect the list of candidate lemmasbefore or after any automated filtering process takes place. The usercan then quickly scanned the remaining list of candidate lemmas andeliminate any that do not belong.

At this point, a mass of unprocessed text in the corpus 202 has beentransformed into a final list of lemmas for the ontology 214. The nextto portions of the language-analysis pipeline, the relationshipassignment engine 210 and the relationship visualization/refinementengine 212, are often considered to be the most time-consuming anddifficult phases of the process. The relationship assignment engine 210can automatically attempt to generate relationships between the lemmasbased on their relative location in the corpus 202, as well as theirpart of speech, dictionary definition, and known synonyms. Therelationship visualization/refinement engine 212 can then be used todisplay the preliminary relationship assignments to a user in agraphical interface. The user can then visually manipulate therelationships displayed in a graph or tree format on the display deviceto generate a final set of relationships between the lemmas. Thecombination of final lemmas 216 and final relationships 218 can then beexported from the language-analysis pipeline as an ontology 214.

In order to generate a French ontology 214-2 and an English ontology214-1, each step in the language-analysis pipeline must be executed foreach individual language. This implies that users may need to manuallyexamine the final lemma list from the lemma filtering/consolidationengine 208, and manually establish the set of final relationships 218.This requires extensive user involvement for generating ontologies, userinvolvement that will often require expertise in more than one language.

FIG. 2B illustrates a language analysis pipeline 200 b using a universalontology, according to some embodiments. As described above, theuniversal ontology 214-1 may be language independent, although it willnecessarily be represented by a particular language, such as English.The universal ontology 214-1 will include a set of lemmas 216-1 and aset of relationships 218-1 for the set of lemmas 216-1. In order togenerate a French ontology 214-2 from the French corpus 202-2, theuniversal ontology 214-1 can be used to eliminate the time-consumingsteps of generating relationships between French lemmas in the languageanalysis pipeline.

As described above, the French corpus 202-2 can be analyzed using acorpus analysis engine 204-2, from which the set of lemmas can begenerated using the lemma generation engine 206-2. After filtering andediting the candidate lemmas using a lemma filtering/consolidationengine 208-2, a final set of lemmas 216-2 for the French ontology 214-2will be generated. At this point, a cross-language lemma mapping engine220 can receive the final set of lemmas 216-2 for the French ontology214-2 and map the meanings of the final set of lemmas 216-2 to thelemmas 216-1 of the universal ontology 214-1.

Methods used by the cross-language lemma mapping engine 220 to match thefinal set of lemmas 216-2 for the French ontology 214-2 to the lemmas216-1 of the universal ontology 214-1 may vary according to theparticular embodiment. In some embodiments, the existing databases canbe used to link lemmas between languages by linking language specificsynsets, or sets of synonym words, to a shared index layer. For example,the Euro WordNet project provides a database that links meanings betweenconcepts of different languages. In other embodiments, an automaticlanguage translation service (e.g., Google translate) can be used togenerate cross-language synonyms. For example, a lemma from the finalset of lemmas 216-2 can be translated into the language of the universalontology 214-1 and mapped to a lemma in the lemmas 216-1 of theuniversal ontology 214-1. In some embodiments, if the language isalready been mapped to the universal ontology, the corpus can be used togenerate lemmas for which there are parallel data in the universalontology language. For example, in a parallel corpus, aligned sentencesfrom the new language are mapped directly to parallel sentences in theuniversal ontology language. Words and multi-token words can be alignedusing well-known statistical methods in both languages. Then, the lemmasand mapping can be generated automatically.

After mapping the final set of lemmas 216-2 to lemmas 216-1 in theuniversal ontology 214-1, the relationships 218-1 in the universalontology can be directly mapped to the final set of lemmas 216-2 by arelationship mapping enigine 222. In many cases, the relationships 218-1can be directly mapped to the final set of lemmas 216-2 in the Frenchontology 214-2 in order to generate French-language relationships 218-2.In some cases, lemmas in the final set of lemmas 216-2 of the Frenchontology 214-2 may not have a direct analog in the lemmas 216-1 of theuniversal ontology 214-1. This situation will be discussed in greaterdetail below. In some embodiments, the relationship mapping engine 222may simply read the relationships from universal ontology 218-1 andapply them directly to the French ontology 214-2, such that theFrench-language relationships 218-2 are the same as the relationships218-1 in the universal ontology. If there are any lemmas that cannot bedirectly mapped between the French language and universal ontology, thenadditional relationships can be manually added if needed, although thisshould be a seldom-used operation.

FIG. 2C illustrates a language analysis pipeline 200 c using lemmatranslation, according to some embodiments. In this embodiment, auniversal ontology 214-1 can be developed for a specific corpus ofmaterial. For example, a master version of a web domain appearingEnglish may be used to generate a universal ontology specific to thatparticular web domain. As will often be the case for multinationalcorporations, the master version of the web domain may be translatedinto various other languages to serve an international customer base.Instead of separately analyzing the translations of the master versionof the web domain appearing in English, the universal ontology 214-1 canbe used to automatically generate ontologies in the various otherlanguages.

For each of the lemmas 216-1 appearing in the universal ontology 214-1,the lemma translation engine 224 can generate a final set of lemmas216-2 in another language, such as French. In some cases, thetranslation of a word in English can result in a set of synonyms thatcould be used in French. For example, the English word for “bicycle”could generate a set of five synonyms in the French language. The Frenchsynonyms can be compared to the actual corpus of the French translationof the web domain to determine which synonym should be used in theFrench ontology 214-2. After generating the final set of lemmas 216-2for the French ontology 214-2, the relationship mapping engine 222 canbe used to generate the relationships 218-2 for the French ontology214-2. Note that in this embodiment, the complete French ontology 214-2was generated based on the universal ontology 214-1 without having toprocess the French corpus of the web domain in the full languageanalysis pipeline.

FIG. 3A illustrates a diagram 300 a of an ontology during lemma mapping,according to some embodiments. At this stage of the process, a set oflemmas for the French language may be generated by the language analysispipeline. For example, a web domain dealing with bike transportation inParis may have been analyzed to generate the lemmas appearing in FIG.3A. By translating the language of the French lemmas to the language ofthe universal ontology (e.g. English), the lemmas of the French languagecan be mapped to lemmas in the universal ontology. As described above,each lemma concept may have one or more synonyms, which can be used tomap concepts between languages. In FIG. 3A, the concepts for a vehicle302, 314 are mapped, the concepts for a bike 304, 316 are mapped, andthe specific types of bikes are mapped, such as a touring bike 306, 320,a motor bike 310, 322, and/or a tandem bike 312, 324.

Note that the concept for a dirt bike 308 does not have an analogousword in the set of French lemmas from the particular Frech corpus. Insome embodiments, differences between languages are to be anticipated,and the lemmas in one language may not necessarily line up directly withlemmas in another language.

FIG. 3B illustrates a diagram 300 b of an ontology during relationshipmapping, according to some embodiments. After using the mapped lemmas ofFIG. 3B, the relationships between lemmas from the universal ontologycan be mapped and duplicated to form the French-language ontology. Asillustrated in diagram 300 b, the French ontology is established using“is a type of” relationships between the vehicle lemma 314, the bikelemma 316, and so forth. Instead of needing a French-language expert toorganize the relationships between the lemmas found in the Frenchcorpus, the existing relationships of the universal ontology can be usedto automatically generate the corresponding relationships in theFrench-language ontology.

FIG. 4A illustrates a system 400 a for generating an ontology using asynonymous word database, according to some embodiments. A languageanalysis pipeline 404 can accept a corpus 402—such as a web domain—asinput to generate lemmas 406. The lemma mapping engine 408 can map thegenerated lemmas to concepts in a universal ontology 412. The lemmamapping engine 408 may also use additional resources to mapped tolemmas, such as a commercially available or proprietary synonymous worddatabase 416 that maps concepts between languages. The lemma mappingengine 408 can access the database 416 through an API or web interface420. Next, the relationship mapping engine 410 can reuse relationshipsfrom the universal ontology 412 to generate a final language specificontology 414 as described above.

FIG. 4B illustrates a system 400 b for generating an ontology using alanguage translation service database, according to some embodiments.The embodiment of system 400 b is similar to that of system 400 a, thedifference being that the API or web interface 424 uses a webtranslation service 422 for generating mappings between the lemmas ofthe corpus 402 and the lemmas of the universal ontology 412.

FIG. 5 illustrates a flowchart 500 of a method for generating ontologiesfor use in natural language processing, according to some embodiments.The method may include receiving a first ontology in a first languagewith lemmas and relationships between the lemmas (502). The firstontology may be a language-independent set of lemma concepts linkedtogether with relationships. The first ontology may be specific to aparticular corpus, such as a web domain or a set of documents. In someembodiments, the first ontology may be generated using a languageanalysis pipeline that extracts and filters lemmas from a corpus andreceives relationships between lemmas from a language expert.

The method may also include receiving a second set of lemmas in a secondlanguage (504). The second language may be different from the firstlanguage used to express the first, or universal, ontology. The secondset of lemmas may be generated from a language analysis pipeline in amanner similar to how the lemmas of the first ontology were generated.In some embodiments, the corpus for the second set of lemmas in thecorpus of the first ontology may be derived from the same corpus indifferent languages. The method may additionally include mapping each ofthe lemmas in the second language to the lemmas in the first language(506). This step may be accomplished by translating the lemmas in thesecond language into a set of synonyms in the first language, and thenidentifying synonyms that appear in the lemmas in the first ontology.Finally, the method may further include generating a second ontology inthe second language by using the relationships in the first ontology tocreate relationships between the lemmas in the second language (508).

It should be appreciated that the specific steps illustrated in FIG. 5provide particular methods of generating ontologies from a universalontology according to various embodiments of the present invention.Other sequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 5 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

Each of the methods described herein may be implemented by a computersystem. Each step of these methods may be executed automatically by thecomputer system, and/or may be provided with inputs/outputs involving auser. For example, a user may provide inputs for each step in a method,and each of these inputs may be in response to a specific outputrequesting such an input, wherein the output is generated by thecomputer system. Each input may be received in response to acorresponding requesting output. Furthermore, inputs may be receivedfrom a user, from another computer system as a data stream, retrievedfrom a memory location, retrieved over a network, requested from a webservice, and/or the like. Likewise, outputs may be provided to a user,to another computer system as a data stream, saved in a memory location,sent over a network, provided to a web service, and/or the like. Inshort, each step of the methods described herein may be performed by acomputer system, and may involve any number of inputs, outputs, and/orrequests to and from the computer system which may or may not involve auser. Those steps not involving a user may be said to be performedautomatically by the computer system without human intervention.Therefore, it will be understood in light of this disclosure, that eachstep of each method described herein may be altered to include an inputand output to and from a user, or may be done automatically by acomputer system without human intervention where any determinations aremade by a processor. Furthermore, some embodiments of each of themethods described herein may be implemented as a set of instructionsstored on a tangible, non-transitory storage medium to form a tangiblesoftware product.

FIG. 6 depicts a simplified diagram of a distributed system 600 forimplementing one of the embodiments. In the illustrated embodiment,distributed system 600 includes one or more client computing devices602, 604, 606, and 608, which are configured to execute and operate aclient application such as a web browser, proprietary client (e.g.,Oracle Forms), or the like over one or more network(s) 610. Server 612may be communicatively coupled with remote client computing devices 602,604, 606, and 608 via network 610.

In various embodiments, server 612 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. In some embodiments, these services may beoffered as web-based or cloud services or under a Software as a Service(SaaS) model to the users of client computing devices 602, 604, 606,and/or 608. Users operating client computing devices 602, 604, 606,and/or 608 may in turn utilize one or more client applications tointeract with server 612 to utilize the services provided by thesecomponents.

In the configuration depicted in the figure, the software components618, 620 and 622 of system 600 are shown as being implemented on server612. In other embodiments, one or more of the components of system 600and/or the services provided by these components may also be implementedby one or more of the client computing devices 602, 604, 606, and/or608. Users operating the client computing devices may then utilize oneor more client applications to use the services provided by thesecomponents. These components may be implemented in hardware, firmware,software, or combinations thereof. It should be appreciated that variousdifferent system configurations are possible, which may be differentfrom distributed system 600. The embodiment shown in the figure is thusone example of a distributed system for implementing an embodimentsystem and is not intended to be limiting.

Client computing devices 602, 604, 606, and/or 608 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 602, 604, 606,and 608 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s) 610.

Although exemplary distributed system 600 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 612.

Network(s) 610 in distributed system 600 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 610 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 610 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.11 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 612 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. In variousembodiments, server 612 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 612 may correspond to a server for performing processingdescribed above according to an embodiment of the present disclosure.

Server 612 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 612 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 612 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 602, 604, 606, and 608. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 612 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 602, 604, 606, and 608.

Distributed system 600 may also include one or more databases 614 and616. Databases 614 and 616 may reside in a variety of locations. By wayof example, one or more of databases 614 and 616 may reside on anon-transitory storage medium local to (and/or resident in) server 612.Alternatively, databases 614 and 616 may be remote from server 612 andin communication with server 612 via a network-based or dedicatedconnection. In one set of embodiments, databases 614 and 616 may residein a storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 612 may be stored locallyon server 612 and/or remotely, as appropriate. In one set ofembodiments, databases 614 and 616 may include relational databases,such as databases provided by Oracle, that are adapted to store, update,and retrieve data in response to SQL-formatted commands.

FIG. 7 is a simplified block diagram of one or more components of asystem environment 700 by which services provided by one or morecomponents of an embodiment system may be offered as cloud services, inaccordance with an embodiment of the present disclosure. In theillustrated embodiment, system environment 700 includes one or moreclient computing devices 704, 706, and 708 that may be used by users tointeract with a cloud infrastructure system 702 that provides cloudservices. The client computing devices may be configured to operate aclient application such as a web browser, a proprietary clientapplication (e.g., Oracle Forms), or some other application, which maybe used by a user of the client computing device to interact with cloudinfrastructure system 702 to use services provided by cloudinfrastructure system 702.

It should be appreciated that cloud infrastructure system 702 depictedin the figure may have other components than those depicted. Further,the embodiment shown in the figure is only one example of a cloudinfrastructure system that may incorporate an embodiment of theinvention. In some other embodiments, cloud infrastructure system 702may have more or fewer components than shown in the figure, may combinetwo or more components, or may have a different configuration orarrangement of components.

Client computing devices 704, 706, and 708 may be devices similar tothose described above for 602, 604, 606, and 608.

Although exemplary system environment 700 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 702.

Network(s) 710 may facilitate communications and exchange of databetween clients 704, 706, and 708 and cloud infrastructure system 702.Each network may be any type of network familiar to those skilled in theart that can support data communications using any of a variety ofcommercially-available protocols, including those described above fornetwork(s) 610.

Cloud infrastructure system 702 may comprise one or more computersand/or servers that may include those described above for server 612.

In certain embodiments, services provided by the cloud infrastructuresystem may include a host of services that are made available to usersof the cloud infrastructure system on demand, such as online datastorage and backup solutions, Web-based e-mail services, hosted officesuites and document collaboration services, database processing, managedtechnical support services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 702 may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

In various embodiments, cloud infrastructure system 702 may be adaptedto automatically provision, manage and track a customer's subscriptionto services offered by cloud infrastructure system 702. Cloudinfrastructure system 702 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 702 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 702 is operatedsolely for a single organization and may provide services for one ormore entities within the organization. The cloud services may also beprovided under a community cloud model in which cloud infrastructuresystem 702 and the services provided by cloud infrastructure system 702are shared by several organizations in a related community. The cloudservices may also be provided under a hybrid cloud model, which is acombination of two or more different models.

In some embodiments, the services provided by cloud infrastructuresystem 702 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 702. Cloud infrastructure system 702 then performs processing toprovide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructuresystem 702 may include, without limitation, application services,platform services and infrastructure services. In some examples,application services may be provided by the cloud infrastructure systemvia a SaaS platform. The SaaS platform may be configured to providecloud services that fall under the SaaS category. For example, the SaaSplatform may provide capabilities to build and deliver a suite ofon-demand applications on an integrated development and deploymentplatform. The SaaS platform may manage and control the underlyingsoftware and infrastructure for providing the SaaS services. Byutilizing the services provided by the SaaS platform, customers canutilize applications executing on the cloud infrastructure system.Customers can acquire the application services without the need forcustomers to purchase separate licenses and support. Various differentSaaS services may be provided. Examples include, without limitation,services that provide solutions for sales performance management,enterprise integration, and business flexibility for largeorganizations.

In some embodiments, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someembodiments, platform services provided by the cloud infrastructuresystem may include database cloud services, middleware cloud services(e.g., Oracle Fusion Middleware services), and Java cloud services. Inone embodiment, database cloud services may support shared servicedeployment models that enable organizations to pool database resourcesand offer customers a Database as a Service in the form of a databasecloud. Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain embodiments, cloud infrastructure system 702 may also includeinfrastructure resources 730 for providing the resources used to providevarious services to customers of the cloud infrastructure system. In oneembodiment, infrastructure resources 730 may include pre-integrated andoptimized combinations of hardware, such as servers, storage, andnetworking resources to execute the services provided by the PaaSplatform and the SaaS platform.

In some embodiments, resources in cloud infrastructure system 702 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 730 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain embodiments, a number of internal shared services 732 may beprovided that are shared by different components or modules of cloudinfrastructure system 702 and by the services provided by cloudinfrastructure system 702. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain embodiments, cloud infrastructure system 702 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one embodiment, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 702, and the like.

In one embodiment, as depicted in the figure, cloud managementfunctionality may be provided by one or more modules, such as an ordermanagement module 720, an order orchestration module 722, an orderprovisioning module 724, an order management and monitoring module 726,and an identity management module 728. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 734, a customer using a client device, such asclient device 704, 706 or 708, may interact with cloud infrastructuresystem 702 by requesting one or more services provided by cloudinfrastructure system 702 and placing an order for a subscription forone or more services offered by cloud infrastructure system 702. Incertain embodiments, the customer may access a cloud User Interface(UI), cloud UI 712, cloud UI 714 and/or cloud UI 716 and place asubscription order via these UIs. The order information received bycloud infrastructure system 702 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 702 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 712, 714 and/or 716.

At operation 736, the order is stored in order database 718. Orderdatabase 718 can be one of several databases operated by cloudinfrastructure system 718 and operated in conjunction with other systemelements.

At operation 738, the order information is forwarded to an ordermanagement module 720. In some instances, order management module 720may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 740, information regarding the order is communicated to anorder orchestration module 722. Order orchestration module 722 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 722 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 724.

In certain embodiments, order orchestration module 722 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 742, upon receiving an order for a newsubscription, order orchestration module 722 sends a request to orderprovisioning module 724 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 724 enables the allocation of resources for the services orderedby the customer. Order provisioning module 724 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 700 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 722 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 744, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 704, 706 and/or 708 by order provisioning module 724 of cloudinfrastructure system 702.

At operation 746, the customer's subscription order may be managed andtracked by an order management and monitoring module 726. In someinstances, order management and monitoring module 726 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain embodiments, cloud infrastructure system 700 may include anidentity management module 728. Identity management module 728 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 700. In someembodiments, identity management module 728 may control informationabout customers who wish to utilize the services provided by cloudinfrastructure system 702. Such information can include information thatauthenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 728 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 8 illustrates an exemplary computer system 800, in which variousembodiments of the present invention may be implemented. The system 800may be used to implement any of the computer systems described above. Asshown in the figure, computer system 800 includes a processing unit 804that communicates with a number of peripheral subsystems via a bussubsystem 802. These peripheral subsystems may include a processingacceleration unit 806, an I/O subsystem 808, a storage subsystem 818 anda communications subsystem 824. Storage subsystem 818 includes tangiblecomputer-readable storage media 822 and a system memory 810.

Bus subsystem 802 provides a mechanism for letting the variouscomponents and subsystems of computer system 800 communicate with eachother as intended. Although bus subsystem 802 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 802 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P 1386.1standard.

Processing unit 804, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 800. One or more processorsmay be included in processing unit 804. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 804 may be implemented as one or more independent processing units832 and/or 834 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 804 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 804 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processor(s)804 and/or in storage subsystem 818. Through suitable programming,processor(s) 804 can provide various functionalities described above.Computer system 800 may additionally include a processing accelerationunit 806, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 808 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Sirit navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system800 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 800 may comprise a storage subsystem 818 that comprisessoftware elements, shown as being currently located within a systemmemory 810. System memory 810 may store program instructions that areloadable and executable on processing unit 804, as well as datagenerated during the execution of these programs.

Depending on the configuration and type of computer system 800, systemmemory 810 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 804. In some implementations, system memory 810 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system800, such as during start-up, may typically be stored in the ROM. By wayof example, and not limitation, system memory 810 also illustratesapplication programs 812, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 814, and an operating system 816. By way ofexample, operating system 816 may include various versions of MicrosoftWindows®, Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, and Palm® OSoperating systems.

Storage subsystem 818 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem818. These software modules or instructions may be executed byprocessing unit 804. Storage subsystem 818 may also provide a repositoryfor storing data used in accordance with the present invention.

Storage subsystem 800 may also include a computer-readable storage mediareader 820 that can further be connected to computer-readable storagemedia 822. Together and, optionally, in combination with system memory810, computer-readable storage media 822 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 822 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computing system 800.

By way of example, computer-readable storage media 822 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 822 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 822 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 800.

Communications subsystem 824 provides an interface to other computersystems and networks. Communications subsystem 824 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 800. For example, communications subsystem 824 mayenable computer system 800 to connect to one or more devices via theInternet. In some embodiments communications subsystem 824 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 802.11 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some embodiments communications subsystem 824 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some embodiments, communications subsystem 824 may also receive inputcommunication in the form of structured and/or unstructured data feeds826, event streams 828, event updates 830, and the like on behalf of oneor more users who may use computer system 800.

By way of example, communications subsystem 824 may be configured toreceive data feeds 826 in real-time from users of social networks and/orother communication services such as Twitter® feeds, Facebook® updates,web feeds such as Rich Site Summary (RSS) feeds, and/or real-timeupdates from one or more third party information sources.

Additionally, communications subsystem 824 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 828 of real-time events and/or event updates 830, that maybe continuous or unbounded in nature with no explicit end. Examples ofapplications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 824 may also be configured to output thestructured and/or unstructured data feeds 826, event streams 828, eventupdates 830, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 800.

Computer system 800 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 800 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

In the foregoing description, for the purposes of explanation, numerousspecific details were set forth in order to provide a thoroughunderstanding of various embodiments of the present invention. It willbe apparent, however, to one skilled in the art that embodiments of thepresent invention may be practiced without some of these specificdetails. In other instances, well-known structures and devices are shownin block diagram form.

The foregoing description provides exemplary embodiments only, and isnot intended to limit the scope, applicability, or configuration of thedisclosure. Rather, the foregoing description of the exemplaryembodiments will provide those skilled in the art with an enablingdescription for implementing an exemplary embodiment. It should beunderstood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope ofthe invention as set forth in the appended claims.

Specific details are given in the foregoing description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may have been shownas components in block diagram form in order not to obscure theembodiments in unnecessary detail. In other instances, well-knowncircuits, processes, algorithms, structures, and techniques may havebeen shown without unnecessary detail in order to avoid obscuring theembodiments.

Also, it is noted that individual embodiments may have beeen describedas a process which is depicted as a flowchart, a flow diagram, a dataflow diagram, a structure diagram, or a block diagram. Although aflowchart may have described the operations as a sequential process,many of the operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in a figure. A process may correspond to a method, afunction, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination can correspond to a return ofthe function to the calling function or the main function.

The term “computer-readable medium” includes, but is not limited toportable or fixed storage devices, optical storage devices, wirelesschannels and various other mediums capable of storing, containing, orcarrying instruction(s) and/or data. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc., may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

Furthermore, embodiments may be implemented by hardware, software,firmware, middleware, microcode, hardware description languages, or anycombination thereof. When implemented in software, firmware, middlewareor microcode, the program code or code segments to perform the necessarytasks may be stored in a machine readable medium. A processor(s) mayperform the necessary tasks.

In the foregoing specification, aspects of the invention are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

Additionally, for the purposes of illustration, methods were describedin a particular order. It should be appreciated that in alternateembodiments, the methods may be performed in a different order than thatdescribed. It should also be appreciated that the methods describedabove may be performed by hardware components or may be embodied insequences of machine-executable instructions, which may be used to causea machine, such as a general-purpose or special-purpose processor orlogic circuits programmed with the instructions to perform the methods.These machine-executable instructions may be stored on one or moremachine readable mediums, such as CD-ROMs or other type of opticaldisks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic oroptical cards, flash memory, or other types of machine-readable mediumssuitable for storing electronic instructions. Alternatively, the methodsmay be performed by a combination of hardware and software.

What is claimed is:
 1. A method of mapping computer domain ontologiesbetween languages, the method comprising: receiving, using a computersystem, a first ontology in a first language, wherein the first ontologyis comprised of a first plurality of lemmas and a plurality ofrelationships between the plurality of lemmas; receiving, using thecomputer system, a second plurality of lemmas in a second language;mapping, using the computer system, each of the second plurality oflemmas in the second language to a respective lemma in the firstplurality of lemmas in the first language; and generating, using thecomputer system, a second ontology in the second language by using theplurality of relationships in the first ontology to create relationshipsbetween the second plurality of lemmas in the second language.
 2. Themethod of claim 1, wherein the first ontology comprises a languageindependent ontology that encodes relationships between concepts thatare represented by the first plurality of lemmas.
 3. The method of claim1, wherein the second plurality of lemmas are generated from a versionof a web domain in the second language.
 4. The method of claim 3,wherein the first plurality of lemmas are generated from a version ofthe web domain in the first language.
 5. The method of claim 1, whereinthe second plurality of lemmas comprise n-grams less than apredetermined length that occur in a corpus at least a predeterminednumber of times.
 6. The method of claim 1, wherein the plurality ofrelationships of the first ontology are directly inserted into thesecond ontology.
 7. The method of claim 1, wherein mapping each of thesecond plurality of lemmas in the second language to a respective lemmain the first plurality of lemmas in the first language comprisestranslating the second plurality of lemmas in the second language intothe first language.
 8. A non-transitory, computer-readable mediumcomprising instructions which, when executed by one or more processors,causes the one or more processors to perform operations comprising:receiving a first ontology in a first language, wherein the firstontology is comprised of a first plurality of lemmas and a plurality ofrelationships between the plurality of lemmas; receiving a secondplurality of lemmas in a second language; mapping each of the secondplurality of lemmas in the second language to a respective lemma in thefirst plurality of lemmas in the first language; and generating a secondontology in the second language by using the plurality of relationshipsin the first ontology to create relationships between the secondplurality of lemmas in the second language.
 9. The non-transitory,computer-readable medium of claim 8, wherein the first ontologycomprises a language independent ontology that encodes relationshipsbetween concepts that are represented by the first plurality of lemmas.10. The non-transitory, computer-readable medium of claim 8, wherein thesecond plurality of lemmas are generated from a version of a web domainin the second language.
 11. The non-transitory, computer-readable mediumof claim 11, wherein the first plurality of lemmas are generated from aversion of the web domain in the first language.
 12. The non-transitory,computer-readable medium of claim 8, wherein the second plurality oflemmas comprise n-grams less than a predetermined length that occur in acorpus at least a predetermined number of times.
 13. The non-transitory,computer-readable medium of claim 8, wherein the plurality ofrelationships of the first ontology are directly inserted into thesecond ontology.
 14. The non-transitory, computer-readable medium ofclaim 8, wherein mapping each of the second plurality of lemmas in thesecond language to a respective lemma in the first plurality of lemmasin the first language comprises translating the second plurality oflemmas in the second language into the first language.
 15. A systemcomprising: one or more processors; and one or more memory devicescomprising instructions which, when executed by the one or moreprocessors, cause the one or more processors to perform operationscomprising: receiving a first ontology in a first language, wherein thefirst ontology is comprised of a first plurality of lemmas and aplurality of relationships between the plurality of lemmas; receiving asecond plurality of lemmas in a second language; mapping each of thesecond plurality of lemmas in the second language to a respective lemmain the first plurality of lemmas in the first language; and generating asecond ontology in the second language by using the plurality ofrelationships in the first ontology to create relationships between thesecond plurality of lemmas in the second language.
 16. The system ofclaim 15, wherein the first ontology comprises a language independentontology that encodes relationships between concepts that arerepresented by the first plurality of lemmas.
 17. The system of claim15, wherein the second plurality of lemmas are generated from a versionof a web domain in the second language.
 18. The system of claim 17,wherein the first plurality of lemmas are generated from a version ofthe web domain in the first language.
 19. The system of claim 15,wherein the second plurality of lemmas comprise n-grams less than apredetermined length that occur in a corpus at least a predeterminednumber of times.
 20. The system of claim 15, wherein the plurality ofrelationships of the first ontology are directly inserted into thesecond ontology.